{"id":"W2183737160","doi":"","title":"Optimization by Variational Bounding","year":2013,"lang":"en","type":"article","venue":"UCL Discovery (University College London)","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"","keywords":"Differentiable function; Mathematics; Bounding overwatch; Upper and lower bounds; Convex function; Optimization problem; Applied mathematics; Probability distribution; Distribution (mathematics); Mathematical optimization; Stochastic gradient descent; Combinatorics; Regular polygon; Mathematical analysis; Computer science; Artificial neural network; Geometry","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00002972456,0.000125549,0.0001229618,0.0001269292,0.000173677,0.00007456434,0.0001662138,0.00007883461,0.0003244763],"category_scores_gemma":[0.000006502988,0.0001558217,0.00005491862,0.0002727639,0.00003265984,0.001368671,0.00005560025,0.00009942398,0.00006897916],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001497163,"about_ca_system_score_gemma":0.00002049886,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001113602,"about_ca_topic_score_gemma":0.000008063334,"domain_scores_codex":[0.9994067,0.00002334956,0.00008057298,0.0001586286,0.0001416881,0.0001890763],"domain_scores_gemma":[0.9996583,0.00003780486,0.00002946049,0.0001643283,0.00005261462,0.00005746095],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000363098,0.00009826112,0.000663988,0.00002701689,0.0001846197,0.00005061654,0.0002435385,0.3493392,0.01471548,0.04351159,0.5904208,0.0007086129],"study_design_scores_gemma":[0.0005825729,0.00003077045,0.001311188,0.00003912296,0.00003494769,0.00001024486,0.000348945,0.9779136,0.001986624,0.000869483,0.01645821,0.0004143601],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2186184,0.00009905449,0.7602047,0.0002946407,0.00035746,0.0003385214,0.0002222167,0.0008666381,0.01899836],"genre_scores_gemma":[0.9839053,0.00005854258,0.010116,0.000074998,0.00004561512,0.000001547382,0.00008385257,0.00002301452,0.005691117],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7652869,"threshold_uncertainty_score":0.6354226,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004430609314108844,"score_gpt":0.155144370784974,"score_spread":0.1507137614708652,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}